mlsanityData to 14 Oct 2026

Deeper Depth Prediction with Fully Convolutional Residual Networks

arXiv 1606.00373 ↗First posted 1 Jun 2016cs.CVcited by 403

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Abstract (arXiv)

This paper addresses the problem of estimating the depth map of a scene given a single RGB image. We propose a fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps. In order to improve the output resolution, we present a novel way to efficiently learn feature map up-sampling within the network. For optimization, we introduce the reverse Huber loss that is particularly suited for the task at hand and driven by the value distributions commonly present in depth maps. Our model is composed of a single architecture that is trained end-to-end and does not rely on post-processing techniques, such as CRFs or other additional refinement steps. As a result, it runs in real-time on images or videos. In the evaluation, we show that the proposed model contains fewer parameters and requires fewer training data than the current state of the art, while outperforming all approaches on depth estimation. Code and models are publicly available.

Key facts. Cited by 403 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jun 2016.

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Citing papers per year: 2021: 103; 2022: 98; 2023: 84; 2024: 52; 2025: 40; 2026: 26.

403 papers.

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  1. 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 657
  2. 2024Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimationcited by 395
  3. 2024UniDepth: Universal Monocular Metric Depth Estimationcited by 382
  4. 2023Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Imagecited by 365
  5. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 327
  6. 2021Neural RGB-D Surface Reconstructioncited by 313
  7. 2022MultiMAE: Multi-modal Multi-task Masked Autoencoderscited by 291
  8. 2021Is Pseudo-Lidar needed for Monocular 3D Object detection?cited by 249
  9. 2025UniDepthV2: Universal Monocular Metric Depth Estimation Made Simplercited by 173
  10. 2021Efficient Visual Pretraining with Contrastive Detectioncited by 150
  11. 2024DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshingcited by 138
  12. 2022MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformercited by 136
38 more
  1. 2021Transformer-Based Attention Networks for Continuous Pixel-Wise Predictioncited by 125
  2. 2022Backbone is All Your Need: A Simplified Architecture for Visual Object Trackingcited by 119
  3. 2022DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimationcited by 118
  4. 2023The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimationcited by 116
  5. 2024Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Imagecited by 116
  6. 2022P3Depth: Monocular Depth Estimation with a Piecewise Planarity Priorcited by 107
  7. 2022Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimationcited by 100
  8. 2022PanoFormer: Panorama Transformer for Indoor 360 Depth Estimationcited by 95
  9. 2022LocalBins: Improving Depth Estimation by Learning Local Distributionscited by 94
  10. 2021UniFuse: Unidirectional Fusion for 360circ Panorama Depth Estimationcited by 92
  11. 2021Characterizing signal propagation to close the performance gap in unnormalized ResNetscited by 88
  12. 2023Monocular Depth Estimation using Diffusion Modelscited by 87
  13. 2021Divide and Contrast: Self-supervised Learning from Uncurated Datacited by 85
  14. 2021Endo-Depth-and-Motion: Reconstruction and Tracking in Endoscopic Videos using Depth Networks and Photometric Constraintscited by 81
  15. 2022ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfercited by 75
  16. 2023iDisc: Internal Discretization for Monocular Depth Estimationcited by 73
  17. 2021Scene-aware Generative Network for Human Motion Synthesiscited by 70
  18. 2021Unsupervised Scale-consistent Depth Learning from Videocited by 69
  19. 2021ROCA: Robust CAD Model Retrieval and Alignment from a Single Imagecited by 67
  20. 2021RGB-D Local Implicit Function for Depth Completion of Transparent Objectscited by 64
  21. 2021Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimationcited by 63
  22. 2025UniK3D: Universal Camera Monocular 3D Estimationcited by 62
  23. 2022OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusioncited by 61
  24. 2021360MonoDepth: High-Resolution 360deg Monocular Depth Estimationcited by 60
  25. 2023Robust Monocular Depth Estimation under Challenging Conditionscited by 58
  26. 2022Human-Aware Object Placement for Visual Environment Reconstructioncited by 56
  27. 2023IEBins: Iterative Elastic Bins for Monocular Depth Estimationcited by 54
  28. 2023LDM3D: Latent Diffusion Model for 3Dcited by 53
  29. 2021Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Predictioncited by 48
  30. 2024Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Failcited by 47
  31. 2021Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometrycited by 46
  32. 2023Improving Deep Regression with Ordinal Entropycited by 45
  33. 2023NDDepth: Normal-Distance Assisted Monocular Depth Estimationcited by 45
  34. 2024ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimationcited by 45
  35. 2021Adaptive Surface Normal Constraint for Depth Estimationcited by 43
  36. 2021Toward Practical Monocular Indoor Depth Estimationcited by 42
  37. 2023VCoder: Versatile Vision Encoders for Multimodal Large Language Modelscited by 42
  38. 2021Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objectscited by 39

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